Pre-operative simulation of trans-catheter valve implantation

ABSTRACT

In a first aspect, the present invention relates to a method for patient-specific virtual percutaneous implantation, comprising estimating a patient-specific anatomical model of a patient-specific aorta based on cardiovascular 2D or 3D medical image data and virtually deploying an implant model representing an implant into said patient-specific anatomical model. In a second aspect, the present invention provides a method for patient-specific virtual percutaneous implantation. In a third aspect, the present invention provides an implant for virtual percutaneous implantation. In a fourth aspect, the present invention provides a system for virtual percutaneous implantation.

FIELD OF THE INVENTION

The present invention relates to the field of pre-operative planning oftrans-catheter valve implantation.

BACKGROUND

The left ventricle of the heart pumps the blood to the aorta through theaortic valve. Aortic (valve) stenosis is a pathology occurring when theaortic valve does not open fully because the leaflets calcify, thickenand stiffen and, as a result, the blood flow going from the heart to thesystemic circulation decreases. Aortic stenosis manifests itself inelderly people, with a prevalence going from 1.3% in over 65 and 4% inover 85 year old people. Currently it is the most common valvular heartdisease in the Western world and its prevalence is increasing with theaging population.

The standard treatment for an aortic stenosis is the Surgical AorticValve Replacement (SAVR) aiming at reproducing the correct function ofthe native valve with an implanted valve. This invasive procedurerequires total anesthesia, sternotomy (open-heart surgery) andcardiopulmonary bypass (the blood is pumped and oxygenated using anexternal machine), and is associated with about 6% in-hospital mortalityfor over 65 year old patients. Moreover, at least one-third of thepatients with severe aortic stenosis are denied valve surgery as therisks associated with surgery are too high.

Trans-catheter aortic valve implantation (TAVI) or trans-catheter aorticvalve repair (TAVR) is a minimally-invasive procedure for treatingaortic stenosis: (1) the valve (e.g. a bioprosthetic valve made ofporcine pericardium sutured on a metal stent) is crimped inside acatheter, (2) the catheter is inserted, for example, in the femoralartery, (3) pushed upstream along the aorta up to the aortic annulus and(4) the new valve is deployed within the diseased native valve. TAVI hasthe potential of treating high-risk patients and replacing the SAVR witha minimally-invasive intervention (no need for open-heart surgery orcardiopulmonary bypass) which can be performed in e.g. about 80 minutes.

Main TAVI complications are vascular injury, stroke, cardiac injury(heart block, coronary obstruction, cardiac perforation), aorticregurgitation, cardiac conduction abnormalities and valve misplacement.Accurate pre operative planning is crucial to select the optimal devicesize and to anticipate potential difficulties.

Unclersizing of a valve implant may lead to paravalvular aorticregurgitation, while oversizing may result in a rupture of the aorticannulus or in a suboptimal functional behavior of the implant. Currentlyavailable planning tools (Philips, Siemens, Pie Medical, Paeion) provideinsights into the patient anatomy and can, for example, be used todetermine the size of the aortic annulus, or to measure the distancebetween the valve plane and the coronary ostia. A problem with thesetools is that they do not provide preoperative insights into theinteraction between a certain implant device and the specific patientanatomy, and can thus not be used to predict complications such asregurgitation. Such insights are extremely valuable for interventionalcardiologists.

Another problem is that is difficult to reconstruct native leaflets frome.g. CT images. In the currently deployed methods, an incomplete leafletimage is obtained, comprising gaps whereby the gaps represent a lack ofdata.

Document US 2011/0153286 A1 discloses a method and system for virtualpercutaneous valve implantation. In one embodiment of the application apatient-specific anatomical model of a heart valve is estimated based on3D cardiac medical image data. An implant model representing a valveimplant is virtually deployed into the patient-specific anatomical modelof the heart valve. A library of implant models, each modelinggeometrical properties of a corresponding valve implant, can bemaintained. The implant models maintained in the library can bevirtually deployed into the patient specific anatomical model of theheart valve to select one of the implant models for use in apercutaneous valve implantation procedure.

US 2011/0153286 A1 does not provide a prediction of the mechanicalbehavior and interaction of the patient-specific aortic root, ascendingaorta and aortic valve leaflets with the deployment of a valve implant.Said document also does not account for calcification of aortic valveleaflets. Neither does it provide a means to study the hemodynamicperformance of an implant deployed in the aortic valve.Balloon-expandable devices whose deployment is based on permanentplastic deformations of the metal cannot be modeled.

There is a need for more precise valve sizing and positioning. Problemis that the aortic annulus is not circular, that the aortic annulus maydeform and that calcium deposits may deform a valve frame.

Another problem is that the aortic root visualised with ComputedTomography (CT) imaging changes in shape and size after TAVI. Also thegeometry of the stent frame of the TAV is affected by the stiffness ofthe aortic root, by the presence of stiff calcified regions and by theexact device position. Sub-optimal treatment planning can have twosocio-economic effects.

At the one hand this gives higher costs for the health system. If theincorrect device/size of the TAV is chosen, the first TAVI procedure mayfail and additional treatments, including a second TAVI procedure(valve-in-valve), SAVR, or rehospitalization may be necessary, with aconsiderable increase of the costs per patient. As a reference, onesingle TAVI procedure costs about 40 k Euro and the stented valve itselfcosts about 20 k Euro. At the other hand this leads to a lowerprognosis. Sub-optimal treatment planning may result in peri-proceduralcomplications, which affect both the life quality and the lifeexpectancy of the patient. An oversized valve may rupture the annulus ordissect the aorta whereas an undersized valve may dislodge and migrateor can induce paravalvular regurgitation.

The aim of the present invention is to provide a solution to overcome atleast part of the above mentioned disadvantages. The invention theretoaims to provide an improved method for preoperative insights into theinteraction of an implant device and specific patient anatomy, forbetter prediction of complications, such as regurgitation, for betterprediction of the hemodynamic performance of an implant deployed in anaortic valve, and for better patient selection and stratification. Alsothe invention aims to provide a web-based pre-operative planning servicefor TAVI using computer simulations that predict stent frame deformationand incomplete frame apposition, allowing to assess the risk onregurgitation and other complications such as coronary obstruction andconduction abnormalities prior to the intervention. Also the inventionaims to give valuable insights for optimal device position, size andtype.

In a further aspect, the invention aims to provide an improved stentdevice obtained by said method and a system for the execution of saidmethod.

SUMMARY

In a first aspect, the present invention aims to provide a method forpatient-specific virtual percutaneous implantation according to claim 1.The method allows accurate prediction of an optimal size of the implant,specifically adapted to the anatomy of the patient. Furthermore, correctpositioning of the implant or optimal implantation depth is predictedand calculated. The method according the current invention will henceminimize the risk of peri- and post-procedural complications. The methodwill also improve patient-specific selection of implants.

In a second aspect, the present invention provides a method according toclaim 17. The method allows for incorporating the impact of surroundingtissue and structures of the aorta and therefore improving accuracy offunctional behavior prediction.

In a third aspect, the present invention provides an implant for virtualpercutaneous implantation according to claim 19.

In a fourth aspect, the present invention provides a system for virtualpercutaneous implantation according to claim 20.

DESCRIPTION OF THE FIGURES

Further features, advantages and objects of the present invention willbecome apparent for the skilled person when reading the followingdetailed description of embodiments of the present invention, when takenin conjunction with the figures of the enclosed drawings.

FIG. 1 describes a triangulated surface of the calcification in theleaflets and of the other leaflet tissue obtained using segmentationsoftware. Automatic segmentation does not lead to a nice geometricalmodel of the three leaflets: parts are connected and gaps exist.

FIG. 2 describes points that define the leaflet geometry. Each leafletis defined by, for example, 25 points. A subset, for example 13, ofthese points lies on the attachment edge that connects the valve withthe aortic root.

FIG. 3 shows an illustration of the different steps of the method of thecurrent invention to generate a computational mesh or grid of thecalcified leaflets.

FIG. 4 and FIG. 5 depict an example of a patient-based anatomical modelin which a device model is deployed using a simulation technique calledfinite element analysis.

FIG. 6 depicts an overview of 10 deformed CoreValve (Medtronic) stentframes.

FIG. 7 depicts a correlation between the maximal diameter (Dmax) of thestent frame measured at the ventricular end. The Dmax measured on thepost-operative CT data is plotted on the X-axis, while the Dmaxpredicted by the finite element analyses (FEA) is given on the Y-axis.

FIG. 8 depicts the deformed structures from the finite element analysis(left panel). The flow during diastole can then be modeled usingcomputational fluid dynamics to assess the amount of regurgitation. Theright panel shows the flow in the plane indicated in the left panel.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to a method for patient-specific virtualpercutaneous implantation as well as to an implant obtained by thecurrent method and a system employing the method of the currentinvention.

The present invention will be described on the basis of the figures anddetailed description of the invention, which complete content is to beregarded as an integral part of the detailed description of theinvention.

Unless otherwise defined, all terms used in disclosing the invention,including technical and scientific terms, have the meaning as commonlyunderstood by one of ordinary skill in the art to which this inventionbelongs. By means of further guidance, term definitions are included tobetter appreciate the teaching of the present invention.

As used herein, the following terms have the following meanings:

“A”, “an”, and “the” as used herein refers to both singular and pluralreferents unless the context clearly dictates otherwise. By way ofexample, “a compartment” refers to one or more than one compartment.

“Comprise,” “comprising,” and “comprises” and “comprised of” as usedherein are synonymous with “include”, “including”, “includes” or“contain”, “containing”, “contains” and are inclusive or open-endedterms that specifies the presence of what follows e.g. component and donot exclude or preclude the presence of additional, non-recitedcomponents, features, element, members, steps, known in the art ordisclosed therein.

“Point” and “node” as used herein are synonymous and are usedinterchangeable herein.

In a first aspect, the current invention comprises a method forpatient-specific virtual percutaneous implantation, comprising:

-   -   estimating a patient-specific anatomical model of a        patient-specific aorta based on cardiovascular 2D or 3D medical        image data comprising:    -   a) using segmentation techniques to create a finite element        aorta mesh based on 2D or 3D medical image data, said aorta mesh        representing a patient-specific aorta, preferably comprising the        aortic root and the ascending aorta;    -   b) using segmentation techniques to create a finite element        aortic valve mesh based on 2D or 3D medical image data, said        aortic valve mesh representing a patient-specific aortic valve,        comprising 2 or 3 valve leaflets;    -   c) said patient-specific anatomical model comprising a        patient-specific aorta model and a patient-specific aortic valve        model;    -   d) said patient-specific anatomical model comprises a finite        element mesh wherein each element of said mesh is featured by a        set of nodes wherein adjacent elements of said element comprise        mutually shared nodes with said element, wherein said element is        featured by tissue dependent parameters and wherein each element        of said mesh can differ in tissue dependent parameters from an        adjacent element of said element of said mesh;    -   virtually deploying an implant model representing an implant        into said patient-specific anatomical model, whereby    -   e) for each of said valve leaflets, selecting a number of        leaflet attachment points on said aorta mesh,    -   f) for each of said valve leaflets, estimating a number of        leaflet points;    -   g) for each of said valve leaflets providing a transformed        leaflet mesh, wherein the elements of said transformed leaflet        mesh are featured by normal or aberrant tissue parameters;    -   h) and wherein said patient-specific anatomical model of a blood        channel comprises said transformed leaflet meshes.

Preferably, said transformed leaflet mesh is defined by said leafletpoints and said leaflet nodes.

In a further aspect, the current invention comprises a method forpatient-specific virtual percutaneous implantation, comprising:

-   -   estimating a patient-specific anatomical model of a        patient-specific aorta based on cardiovascular 2D or 3D medical        image data comprising:    -   a) using segmentation techniques to create a finite element        aorta mesh based on 2D or 3D medical image data, said aorta mesh        representing a patient-specific aorta, preferably comprising the        aortic root and the ascending aorta;    -   b) using segmentation techniques to create a finite element        aortic valve mesh based on 2D or 3D medical image data, said        aortic valve mesh representing a patient-specific aortic valve,        comprising 2 or 3 valve leaflets;    -   c) said patient-specific anatomical model comprising a        patient-specific aorta model and a patient-specific aortic valve        model;    -   d) said patient-specific anatomical model comprises a finite        element mesh wherein each element of said mesh is featured by a        set of nodes wherein adjacent elements of said element comprise        mutually shared nodes with said element, wherein said element is        featured by tissue dependent parameters and wherein each element        of said mesh can differ in tissue dependent parameters from an        adjacent element of said element of said mesh;    -   virtually deploying an implant model representing an implant        into said patient-specific anatomical model, whereby    -   e) for each of said valve leaflets, selecting a number of        leaflet attachment points on said aorta mesh,    -   f) for each of said valve leaflets, estimating a number of        leaflet points;    -   g) for each of said valve leaflets, mapping said leaflet points        and said leaflet nodes to a transformed leaflet mesh, wherein        the elements of said transformed leaflet mesh are featured by        normal or aberrant tissue parameters;    -   h) and wherein said patient-specific anatomical model of a blood        channel comprises said transformed leaflet meshes.

The term ‘cardiovascular 2D or 3D medical image data’ as used herein isto be understood as all data related to an object to be analysed, saiddata is obtained by 2D or 3D imaging means. 2D or 3D imaging means maycomprise, but are not limited to Nuclear Magnetic Resonance (NMR)imaging, Computed Tomography (CT) imaging, Positron Emission Tomography(PET), Single-photon emission computed tomography (SPECT) andechographic imaging. Preferably, said cardiovascular 2D or 3D medicalimage data is obtained through computed tomography.

The phrase “selecting a number of leaflet attachment points” as usedherein is to be understood as a number of leaflet attachment points ischosen, selected and/or positioned on said aorta mesh.

The phrase “estimating a number of leaflet points” as used herein is tobe understood as a number and/or the position of said leaflet points isestimated.

The term “mapping to” in the current invention is to be understood asassigning properties of said leaflet attachment points and said leafletnodes to said transformed leaflet mesh. Preferably said propertiescomprise a 3-dimensional location. As such said transformed leaflet meshis overlayed with said leaflet attachment points and said leaflet nodes.

The finite element mesh is programmed to contain the material andstructural properties which define how the structure will react tocertain loading conditions. Nodes are assigned at a certain densitythroughout the material depending on the anticipated stress levels of aparticular area. Regions which will receive large amounts of stressusually have a higher node density than those which experience little orno stress. Points of interest may consist of: fracture point ofpreviously tested material, fillets, corners, complex detail, and highstress areas.

A mesh transformation technique is used to create a grid or mesh ofconstant or variable thickness based on anatomical data of non-calcifiedleaflets, but calcifications may lead to higher local thicknesses.Preferably, said transformed leaflet mesh is an isoparametric leafletmesh.

The method of the present invention is advantageous as it improves theaccuracy of the medical intervention, clinical outcomes and reduces theassociated risks. The method provides a better medical prediction of thefunctional behavior of the implant procedure. It allows to betterunderstand the performance of an implant device or how to efficientlydeploy an implant device into a patient. Also the method allows for anapplicability of parameters sufficiently accurate for a broad populationtogether with an acceptable calculation time. Testing new implantdevices in realistic anatomies can be performed early in the developmentcycle to better understand the performance of a new device.

Preferably, said patient-specific model and implant model is a3-dimensional finite element model.

It is also aim of the invention to provide pre-operative insights fordifferent types of procedures (transcatheter aortic valve implantation,transcatheter mitral valve repair, endovascular aneurysm repair, leftatrial appendage closure devices, etc.). The patient-specific model willthen of course be based on a different part of the cardiovascularsystem, and the output will also be different.

Preferably, an alternative for said patient-specific aorta comprises apatient-specific blood vessel and said blood vessel comprises a valve.

The invention can also be applied for other heart valves, so not onlythe aortic valves. Therefore, the term “aorta” in underlying inventioncould also be interpreted as another patient-specific blood vessel e.g.the left atrium and/or ventricle, wherein “aortic valve” could beinterpreted as the mitral valve and wherein correspondingpatient-specific models are incorporated in the invention. Other valvessuch as the pulmonary valve in the pulmonary artery should beincorporated in the invention as well.

More preferably, said blood vessel comprises a patient-specific leftatrium and/or ventricle, and said valve comprises a mitral valve.

More preferably, said blood vessel comprises a patient-specific rightatrium and/or ventricle, and said valve comprises a tricuspid valve.

Preferably, stiffness elements are provided to a plurality of nodes ofsaid aorta mesh, wherein a stiffness element induces a reacting force onthe corresponding node of said aorta mesh, wherein said force isdependent on the displacement of said node or on the distance betweensaid node and a fixed position equal or very close to the initialposition of said node.

The deformation of the aorta and the annulus is affected by thesurrounding tissue and structures. The mechanical impact of thesetissues is important for a correct prediction of the stent structureobtained from simulated TAVI. The surrounding tissue could theoreticallybe included in the model by also segmenting based on the medical images.The making of such a model with all the surrounding tissues would bevery time consuming, and simulation time would increase significantly.This method is advantageous as the virtual percutaneous implantationtakes the impact of surrounding tissue and structures of the aorta intoaccount, such as the stiffness or resistance. So if e.g. the aortadeforms, these stiffness elements (said displacements) will inducereacting forces which will work against this distortion. This way abetter accuracy of simulation and therefore functional behaviorprediction is achieved along with an acceptable calculation orsimulation time.

More preferably, said reacting force depends on said displacements via alinear relationship. One can determine a spring constant k determiningsaid linearity. A linear equation could therefore be Freact=k x, withFreact the reacting force and x said displacement. The spring constant krepresents the stiffness or resistance of the surrounding tissue andstructures of the aorta.

In another more preferred embodiment of the current invention, saidreacting force depends on said displacement via a non-linearrelationship.

More preferably, between different zones of said aorta mesh differentstiffnesses or dependencies are assigned. Said different zones can havedifferent material or tissue dependent properties.

Preferably, said steps a) and b) of the method according to the currentinvention further comprise:

-   -   manually adjusting said leaflet attachment points and/or        manually adjusting said leaflet points.

Preferably, an alternative for said patient-specific anatomical modelcomprises a patient-based anatomical model wherein said steps a, b and care replaceable by:

-   -   creating a finite element aorta mesh based on 2D or 3D medical        image data, said aorta mesh representing a patient-based aorta,        preferably comprising the aortic root and the ascending aorta;    -   creating a finite element aortic valve mesh based on 2D or 3D        medical image data, said aortic valve mesh representing a        patient-based aortic valve, comprising 2 or 3 valve leaflets;    -   said patient-based anatomical model comprising a patient-based        aorta model and a patient-based aortic valve model (43).

The term ‘patient-based model’ as used herein is to be understood as ageneric and/or parametric adaptable model. Based on measurement(s)and/or 2D or 3D medical images of a patient, certain parameters will bedetermined for the patient-based model.

It is advantageous as a patient-based anatomical model can also giveinsights into pre-operative planning of trans-catheter valveimplantation, without the need of segmentation techniques.

Preferably, the method according to the current invention furthercomprises:

-   -   overlaying said transformed leaflet meshes with said aortic        valve mesh;    -   creating an aberrant leaflet mesh, comprising elements wherein        said elements overlap with said aortic valve mesh and wherein        said elements do not overlap with said transformed leaflet        meshes and wherein said elements are featured by aberrant tissue        parameters; wherein said patient-specific anatomical model of a        blood channel comprises said transformed leaflet meshes and said        aberrant leaflet mesh.

It is advantageous as the method also accounts for aberrant tissue suchas e.g. calcifications for patients with aortic stenosis. This leads toa more accurate patient-specific model, which will give rise to betterfunctional behavior prediction and clinical outcomes and again reducesthe associated risks.

In a preferred embodiment, said implant model will comprise a skirt andleaflet elements.

In a more preferred embodiment, said implant model will comprise astent, to be implanted in a passage or conduit of the body of thepatient. In the current invention, said passage or conduit is preferablya vessel, such as an aorta.

More preferably, said implant model comprises a finite element meshwherein each element of said mesh is featured by a set of nodes whereinadjacent elements of said element comprise mutually shared nodes withsaid element, wherein said element is featured by material dependentparameters and wherein each element of said mesh can differ in materialdependent parameters from an adjacent element of said element of saidmesh.

It is advantageous to take into account different material parameters inorder to provide even more accurate results in functional behaviorprediction.

In a further preferred embodiment of the current invention, the methodstep of virtually deploying an implant model representing an implantinto the patient-specific anatomical model comprises a three-dimensionalfinite element analysis. The finite element method (FEM) (its practicalapplication often known as finite element analysis (FEA)) is a solutiontechnique based on subdividing the volume into a large number of smallelements of simple shape, the behavior of which can be described infunction of a small number of variables at a limited number of points(nodes) of the element. For steady state problems this reduces thepartial differential equations to a large set of algebraic equationswhich can be easily solved. Time dependent problems will result in a setof ordinary different equations in time which can numerically beintegrated using standard techniques, like finite difference methods.

Each FEA program may come with an element library, or one is constructedover time. Some sample elements are rod elements, beam elements,plate/shell/composite elements, shear panel, solid elements, springelements, mass elements, rigid elements or viscous damping elements.

Preferably, said tissue dependent parameter and/or a material dependentparameter is chosen from the following group of parameters: Poisson'sratio, Young's modulus, density, shear modulus, yield stress, ultimatestress, elasto-plastic parameter, energy density function, materialdamage parameter, superelastic material parameter, shape-memory materialparameter, isotropic material parameter and anisotropic materialparameter.

It is aim of the invention to provide patient-specific modelssufficiently accurate by taking into account mechanical materialproperties of e.g. different layers, areas of layers, etc., togetherwith a sufficiently fast calculating time.

The method of the current invention comprises furthermore preferably astep whereby a blood mesh is created, comprising a finite volume mesh,wherein the blood mesh describes the volume within said patient-specificanatomical model exclusive said deployed implant model; and whereby theblood flow is calculated for said blood mesh using computational fluiddynamics analysis.

The finite volume method is a method for representing and evaluatingpartial differential equations in the form of algebraic equations[LeVeque, 2002; Toro, 1999]. Similar to the finite difference method orfinite element method, values are calculated at discrete places on ameshed geometry. “Finite volume” refers to the small volume surroundingeach node point on a mesh. In the finite volume method, volume integralsin a partial differential equation that contain a divergence term areconverted to surface integrals, using the divergence theorem. Theseterms are then evaluated as fluxes at the surfaces of each finitevolume. Because the flux entering a given volume is identical to thatleaving the adjacent volume, these methods are conservative. Anotheradvantage of the finite volume method is that it is easily formulated toallow for unstructured meshes. The method is used in many computationalfluid dynamics packages. For more info on the finite volume method, werefer integrally to Eymard et al., Handbook of Numeral Analysis (2000).

In a further preferred step, the amount of paravalvular regurgitation ispredicted based on a geometrical analysis of said blood mesh.Hemodynamic performance of the implant can be predicted by quantifyingparavalvular leakages, valve insufficiency, and effective orifice andsystolic gradients across the aortic prosthesis.

It is advantageous to predict paravalvular regurgitation as it has beenassociated with increased mortality. This regurgitation is the result ofseveral factors related to the aortic root anatomy and its relation tothe implanted prosthesis including the shape and size of the aorticannulus, degree of annular and leaflet calcifications, left-ventricularoutflow tract (LVOT) anatomy, and the prosthesis/annulus discongruence.

More preferably, the position and the orientation for implanting theimplant is determined based on the complications predicted by saidvirtually deployed implant model. Even more preferably, saidcomplications comprise the amount of paravalvular regurgitation,obstruction of coronary artery and/or electrical conductivity problems(e.g. related to an implementation of a pacemaker). Most preferably, theposition and the orientation for implanting the implant is determinedbased on the amount of paravalvular regurgitation predicted by saidvirtually deployed implant model.

This will improve the accuracy of the medical deploying intervention,clinical outcomes and reduce associated risks.

In a further preferred step, the risk of coronary obstruction, annularrupture and conduction abnormalities is predicted by virtually deployingan implant model representing an implant into the patient-specificanatomical model.

The model parameters of the implant model and the patient-specificanatomical model are calibrated by comparing said calculations withpostoperative medical image data.

It is advantageous to determine or calibrate model parameters andspecifications in order to provide improving and/or optimal results fora broad population of patients or a number of subpopulations ofpatients. These parameters can be material dependent parameters, tissuedependent parameters, layer thicknesses, etc.

Preferably, said virtually deploying of an implant model representing animplant into said patient-specific anatomical model comprises:

-   -   maintaining a library of implant models, each modeling        geometrical and/or material properties of a corresponding        implant; and    -   virtually deploying each of a plurality of the implant models        maintained in the library into the patient specific anatomical        model to select one of the plurality of the implant models        maintained in the library for a percutaneous implantation        procedure.

By providing a database of device models representing the actual devicegeometry and having similar mechanical behavior, interventionalcardiologists or hospitals can select optimal device size and type forpatients.

More preferably, this will equally comprise:

-   -   maintaining a library of patient-specific anatomical models,        each modeling geometrical and/or material properties of a        corresponding patient-specific anatomical blood channel; and    -   virtually deploying said implant model into each of a plurality        of the patient specific anatomical models maintained in said        library to evaluate said implant model for a percutaneous        implantation procedure.

This method is advantageous for the development of implant models basedon the functional behavior on a plurality of patient specific anatomicalmodels. This leads to better, cheaper, more effective implants, anoptimal understanding of an implant model and therefore less risk forpatients.

Taking the expansion of an implant model into account will lead to abetter understanding in functional behavior of the implant model.

In a second aspect, the current invention discloses an implant forvirtual percutaneous implantation, obtained by the method as explainedabove.

Preferably, said implant for virtual percutaneous implantation isobtained by selecting an implant model based on the amount ofparavalvular regurgitation predicted by said virtually deployed implantmodel.

In a preferred embodiment, the valve implant is balloon-expendable orself-expendable.

In a third aspect, the current invention discloses a method forpatient-specific virtual percutaneous implantation, comprising:

-   -   estimating a patient-specific anatomical model of a        patient-specific aorta based on cardiovascular 2D or 3D medical        image data comprising:        -   a) using segmentation techniques to create a finite element            aorta mesh based on 2D or 3D medical image data, said aorta            mesh representing a patient-specific aorta, preferably            comprising the aortic root and the ascending aorta;        -   b) using segmentation techniques to create a finite element            aortic valve mesh based on 2D or 3D medical image data, said            aortic valve mesh representing a patient-specific aortic            valve, comprising 2 or 3 valve leaflets;        -   c) said patient-specific anatomical model comprising a            patient-specific aorta model and a patient-specific aortic            valve model;        -   d) said patient-specific anatomical model comprises a finite            element mesh wherein each element of said mesh is featured            by a set of nodes wherein adjacent elements of said element            comprise mutually shared nodes with said element, wherein            said element is featured by tissue dependent parameters and            wherein each element of said mesh can differ in tissue            dependent parameters from an adjacent element of said            element of said mesh;    -   virtually deploying an implant model representing an implant        into said patient-specific anatomical model,        whereby stiffness elements are provided to a plurality of nodes        of said aorta mesh, wherein a stiffness element induces a        reacting force on the corresponding node of said aorta mesh,        wherein said force is dependent on the displacement of said node        or on the distance between said node and a fixed position equal        or very close to the initial position of said node.

The deformation of the aorta and the annulus is affected by thesurrounding tissue and structures. The mechanical impact of thesetissues is important for a correct prediction of the stent structureobtained from simulated TAVI. The surrounding tissue could theoreticallybe included in the model by also segmenting based on the medical images.The making of such a model with all the surrounding tissues would bevery time consuming, and simulation time would increase significantly.This method is advantageous as the virtual percutaneous implantationtakes the impact of surrounding tissue and structures of the aorta intoaccount, such as the stiffness or resistance. So if e.g. the aortadeforms, these stiffness elements (said displacements) will inducereacting forces which will work against this distortion. This way abetter accuracy of simulation and therefore functional behaviorprediction is achieved along with an acceptable calculation orsimulation time.

Preferably, said reacting force depends on said displacements via alinear relationship. One can determine a spring constant k determiningsaid linearity. A linear equation could therefore be Freact=k·x, withFreact the reacting force and x said displacement. The spring constant krepresents the stiffness or resistance of the surrounding tissue andstructures of the aorta.

In another preferred embodiment of the current invention, said reactingforce depends on said displacement via a non-linear relationship.

Preferably, between different zones of said aorta mesh differentstiffnesses or dependencies are assigned. Said different zones can havedifferent material or tissue dependent properties.

Preferably, an alternative for said patient-specific aorta comprises apatient-specific left atrium and left atrial appendage and wherein saidsteps b and c are replaceable by:

-   -   said patient-specific anatomical model comprising a        patient-specific aorta model.

The left atrium comprises a muscular pouch called the “left atrialappendage”. Some people can have blood clots formed in this appendix. Toprevent these clots from going into the bloodstream, implant devicesexist that can be inserted in this appendix in order to blocking clots.This procedure is called “left atrial appendage closure (LAAC)”. Theinvention therefore likewise can improve this LAAC intervention andprocedure.

Preferably, an alternative for said patient-specific aorta comprises apatient-specific blood vessel and said blood vessel comprises a valve.

The invention can also be applied for other heart valves, so not onlythe aortic valves. Therefore, the term “aorta” in underlying inventioncould also be interpreted as another patient-specific blood vessel e.g.the left atrium and/or ventricle, wherein “aortic valve” could beinterpreted as the mitral valve and wherein correspondingpatient-specific models are incorporated in the invention. Other valvessuch as the pulmonary valve in the pulmonary artery should beincorporated in the invention as well.

More preferably, said blood vessel comprises a patient-specific leftatrium and/or ventricle, and said valve comprises a mitral valve.

More preferably, said blood vessel comprises a patient-specific rightatrium and/or ventricle, and said valve comprises a tricuspid valve.

In a final aspect, the current invention discloses a system for virtualpercutaneous implantation, comprising:

-   -   a storage medium comprising 2D or 3D medical image data;    -   a secured server;    -   a computing system implemented with a method for virtual        percutaneous implantation according to the current invention.

Preferably, said storage medium, said secured server and said computingsystem are web-connected.

It is the aim of the current invention to provide a report to themedical doctor, comprising:

-   -   figures of the implanted device(s),    -   colour plots of the incomplete apposition of the device(s).        These plots give insight into possible paravalvular leaks,    -   forces on the annulus f aortic root and stresses in the        annulus/aortic root,    -   a quantitative value derived from the computational fluid        dynamics analysis the geometrical analysis of the blood mesh)        reflecting the amount of regurgitation.

It is also the aim of the current invention to provide 3D models showingthe interaction between one or more implant models representing implantsand a patient-specific anatomical model.

The values currently used in clinical practice describe the amount ofregurgitation (grade 0, 1, 2, 3 or 4; grade 0 corresponds with no orminimal regurgitation, grade 1 is mild regurgitation, grade 2 ismoderate regurgitation, grade 3 is moderately severe regurgitation,grade 4 is severe regurgitation). In clinical practice, aorticregurgitation after TAVI can be observed using medical imaging(angiogram, echo). The physicians then assign a grade to the amount ofregurgitation that they observe. In the current invention, aquantitative value based on, for example, the volume of backflow(regurgitation) during diastole (when the valve is closed) will bederived from the computational fluid dynamics analyses

EXAMPLES Example 1

In a preferred embodiment of the invention an anatomical model of theaortic root and of the ascending aorta is generated from CT images usingtraditional image segmentation methods or software. The resulting 3Danatomical model is a triangulated surface mesh.

FIG. 1 describes a triangulated surface of the calcification in theleaflets and of the other leaflet tissue obtained using segmentationmethods. As mentioned, automatic segmentation does not lead to a nicegeometrical model of the three leaflets: parts are connected and gapsexist. FIG. 1 shows an aortic valve mesh 1, a triangulated finiteelement mesh of normal leaflet tissue 2 and a triangulated finiteelement mesh of calcificied leaflet tissue 3. Parts of the nativeleaflets are visible on CT images but gaps exist. It is thereforedifficult to reconstruct these regions using segmentation methods. Incontrast to known methods, valves according to the present invention arenot based on 4 estimated landmarks, and do not use a hyperbolicparaboloid. In the method according to the current invention, each valveis defined by, for example, 25 points in order to accurately model thereal valve geometry:

-   -   For example, 13 points, lying on the attachment edge, are        manually picked on the previously generated aorta model,    -   the other 12 points are then initially estimated based on the 13        picked points,    -   all points are then manually adjusted in order to match the        shape of the valve model with the geometrical valve information        obtained by segmentation (see FIG. 1).

FIG. 2 describes points that define the leaflet geometry. Each leafletis defined by, for example, 25 points. For example, 13 of these pointslie on the attachment edge that connects the valve with the aortic root.FIG. 2 shows a 3-leaflet valve node geometry 10, a first leaflet node onattachment edge 11, an estimated leaflet 1 node 14, a leaflet 2 node onattachment edge 12, a second estimated leaflet node 15, a leaflet 3 nodeon attachment edge 13 and a third estimated leaflet 3 node 16.

FIG. 3 shows an illustration of the different steps in the proposedmethod to generate a computational mesh or grid of the calcifiedleaflets. A generic leaflet mesh is transformed using an isoparametrictransformation to create a grid or mesh of constant or variablethickness. The thickness is based on anatomical data of non calcifiedleaflets, but calcifications may lead to higher local thicknesses. Inthe top right panel of FIG. 3, the calcification obtained through imagesegmentation is overlayed with the leaflet mesh. In order to incorporatethe calcification in the computational mesh or grid, additional layersof elements are added. From these additional layers of elements, onlythe elements within the calcified regions are kept in the final model.

Element 20 depicts the transformed finite elements 24 of leaflet 1, thetransformed finite elements 25 of leaflet 2 and the transformed finiteelements 26 of leaflet 3. Element 21 comprises overlayed calcifiedregion elements 27. Element 22 comprises an additional layer finiteelements 28 of leaflet 1, an additional layer finite elements 29 ofleaflet 2 and an additional layer finite elements 30 of leaflet 3.Element 23 comprises calcified elements 31 of leaflet 1, calcifiedelements 32 of leaflet 2 and calcified elements 33 of leaflet 3.

FIG. 4 describes examples of a patient-based anatomical model in which adevice model is deployed using a simulation technique called finiteelement analysis.

Example 70 comprises a native ascending aorta model 40, a native aorticroot model 41, a native left ventricle 42, a native aortic leaflet model43, a native aortic valve annulus 44 and a native left coronary artery45. Example 71 comprises a predilated balloon 46. Example 72 comprisesan implant model 47.

An example of a simulated deployment of a self-expandable CoreValve(Medtronic) transcatheter aortic valve is shown in FIGS. 4 and 5. Apredilatation of the calcified valve can be included in the simulationprocess as depicted in FIG. 4 (valvuloplasty). This leads to a weakeningof the calcifications and is often performed in clinical practice.Similarly, a postdilatation of the stent frame, sometimes performed toimprove the expansion of the implanted device, can be included in thesimulation process (not shown). FIG. 4 shows an example of apatient-based anatomical model in which a predilatation is performedusing a simulation technique called finite element analysis and a devicemodel is positioned.

FIG. 5 shows an example of a patient based anatomical model in which adevice model is deployed using a simulation technique called finiteelement analysis. The interaction with the surrounding anatomicalstructures (e.g. heart muscle) is taken into account in the model byadding springs on the aortic surface. The spring stiffness can becontrolled and can have different values in different regions (e.g.higher stiffness near the aortic annulus). Other important parameters inthe model are the material behaviour of the different components(leaflets, calcifications on the leaflets, aortic tissue) and thethickness of the aortic wall. In our model, different wall behaviour andthickness can be assigned to different regions of the aorta. Example 73and example 74 comprise an implant model 47. Example 75 comprises adeployed implant model 48 and a device frame model 49.

FIG. 6 describes an overview of the 10 deformed CoreValve stent frames.FIG. 6 shows a predicted stent frame 50, a 3D reconstruction of stentframe based on post-operative CT data 51 and 10 deformed CoreValve stentframes 54. The model parameters have been calibrated by using pre- andpost-operative CT data of minimum 10 patients that underwent TAVI. Apatient-specific model has been created for all these patients and thesame TAVI procedure was performed using finite element computersimulations as it was done in the hospital (same balloon forpre-dilatation and postdilatation, same size of the CoreValve device,same device position, etc.). Further calibration will be done by addingmore patients to the database. The model parameters were adjusted untila good correlation was obtained between the deformed stent frame aspredicted by the simulations and the geometry of the stent frame asobserved from the post-operative image data (see FIGS. 5 and 6).

FIG. 7 describes the correlation between the maximal diameter (Dmax) ofan elliptic cross section of the stent frame measured at the ventricularend. The Dmax measured on the post-operative CT data (Dmax, CT 53) isplotted on the X-axis, while the Dmax predicted by the finite elementanalyses (Dmax, predicted (FEA) 52) is given on the Y-axis. 57:CoreValve. 26 mm stent 55 and CoreValve. 29 mm stent 56.

FIG. 8 depicts the deformed structures from the finite element analysis(left panel). The flow during diastole can then be modeled usingcomputational fluid dynamics to assess the amount of regurgitation. Theright panel shows the flow in the plane indicated in the left panel.

In FIG. 8 following elements are shown: a native ascending aorta model40, a native aortic root model 41, a native left ventricle 42, a nativeaortic leaflet model 43, a native left coronary artery 45, a rightcoronary artery 60, an implant device leaflet 61, a device frame model49, a computational grid of the fluid domain 62 with a computed flowduring diastole 63.

Based on the deformed structures (stent, aorta, native leaflets, etc.)obtained from the finite element simulation, a computational grid of thefluid domain is created. The generation of such a grid or mesh iscomplex but required to model the backflow or regurgitation usingcomputational fluid dynamics. The following method has been developed tocreate this computational mesh: —a regular grid of small cubes orhexahedral elements is created within the bounding box of thepatient-based model—the cubes within the fluid domain are automaticallydetected. Other approaches are also possible.

Although the present invention has been described with reference topreferred embodiments thereof, many modifications and alternations maybe made by a person having ordinary skill in the art without departingfrom the scope of this invention which is defined by the appendedclaims.

1. A computer-implemented method for evaluating the placement of aprosthetic device in a patient's body, comprising: generating adepiction of a first anatomical structure of the patient's body in whichthe prosthetic device is to be placed, the depiction including a bloodpool volume communicating with an adjacent anatomical structure;designating a placement of the prosthetic device within the firstanatomical structure; and predicting an amount of blood flow obstructionto the adjacent anatomical structure resulting from the placement of theprosthetic device.
 2. The method of claim 1, further comprising revisingthe placement of the prosthetic device responsive to the predictedamount of blood flow obstruction to the adjacent anatomical structure.3. The method of claim 1, wherein for a given placement, predicting theamount of blood flow obstruction to the adjacent anatomical structurecomprises: determining a cross-sectional area of the blood pool volumecommunicating with the adjacent anatomical structure after placement ofthe prosthetic device; and predicting the amount of blood flowobstruction based on the determined cross-sectional area.
 4. The methodof claim 1, wherein designating the placement of the prosthetic devicewithin the first anatomical structure further comprises selecting amodel of the prosthetic device from amongst a plurality of models; andwherein predicting the amount of blood flow obstruction to the adjacentanatomical structure comprises, predicting for the selected model, theamount of blood flow obstruction to the adjacent anatomical structure.5. The method of claim 1, wherein the depiction of the first anatomicalstructure shows an aortic root of the patient, the adjacent anatomicalstructure includes the coronary artery ostia, and predicting an amountof blood flow obstruction to the adjacent anatomical structure resultingfrom the placement of the prosthetic device comprises predicting anamount of obstruction of the blood flow to the coronary artery ostiabased on placement of the prosthetic device within the aortic root. 6.The method of claim 1, wherein the depiction of the first anatomicalstructure shows a mitral valve annulus of the patient, the adjacentanatomical structure includes a left ventricular outflow tract, andpredicting an amount of blood flow obstruction to the adjacentanatomical structure resulting from the placement of the prostheticdevice comprises predicting an amount of blood flow obstruction throughthe left ventricular outflow tract based on placement of the prostheticdevice within the mitral valve annulus.
 7. The method of claim 1 furthercomprising, following designating a placement of the prosthetic devicewithin the first anatomical structure: generating a revised depiction ofthe first anatomical structure showing a deformation of the firstanatomical structure caused by the placement of the prosthetic device,the revised depiction including an impact of the deformation on theblood pool volume and the adjacent anatomical structure.
 8. The methodof claim 7, wherein the deformation of the first anatomical structure iscomputed using finite element analysis.
 9. The method of claim 1,wherein generating the depiction of a first anatomical structurecomprises importing portions of CT-scans of the patient's body.
 10. Themethod of claim 9, wherein the first anatomical structure comprises ahuman heart valve, and generating the depiction of the first anatomicalstructure further comprises generating a depiction of a valve leaflet ofthe human heart valve using a plurality of leaflet attachment points.11. A system or evaluating the placement of a prosthetic device in apatient's body, comprising: a processor; and a storage device havinginstructions stored therein, wherein the processor is configured toaccess the storage device and execute the instructions stored therein,wherein the instructions are configured to: generate a depiction of afirst anatomical structure of the patient's body in which the prostheticdevice is to be placed, the depiction including a blood pool volume thatcommunicates with an adjacent anatomical structure; and for a designatedplacement of the prosthetic device within the first anatomicalstructure, predict an amount of blood flow obstruction to the adjacentanatomical structure.
 12. The system of claim 11, wherein the processoris further configured to revise the placement of the prosthetic deviceresponsive to the predicted amount of blood flow obstruction to theadjacent anatomical structure.
 13. The system of claim 11, wherein for agiven placement, the processor is configured to predict the amount ofblood flow obstruction to the adjacent anatomical structure by:determining a cross-sectional area of the blood pool volumecommunicating with the adjacent anatomical structure after placement ofthe prosthetic device; and predicting the blood flow obstruction basedon the determined cross-sectional area.
 14. The system of claim 11,wherein the processor is configured to: designate placement of theprosthetic device within the first anatomical structure by selecting amodel of the prosthetic device from amongst a plurality of models; andpredict the amount of blood flow obstruction to the adjacent anatomicalstructure resulting from placement of the selected model.
 15. The systemof claim 11, wherein the depiction of the first anatomical structureshows an aortic root of the patient and the adjacent anatomicalstructure includes the coronary artery ostia, and the processor isfurther configured to predict an amount of blood flow obstruction to thecoronary artery ostia based on placement of the prosthetic device withinthe aortic root.
 16. The system of claim 11, wherein the depiction ofthe first anatomical structure shows a mitral valve annulus of thepatient and the adjacent anatomical structure includes a leftventricular outflow tract, and the processor is further configured topredict an amount of blood flow obstruction the through the leftventricular outflow tract based on placement of the prosthetic devicewithin the mitral valve annulus.
 17. The system of claim 11, whereinfollowing the designated placement of the prosthetic device within thefirst anatomical structure, the processor is further configured togenerate a revised depiction of the first anatomical structure showing adeformation of the anatomical structure caused by the designatedplacement of the prosthetic device, the revised depiction including animpact of the deformation on the blood pool volume and the adjacentanatomical structure.
 18. The system of claim 17, wherein the processoris configured to compute the deformation of the anatomical structureusing finite element analysis.
 19. The system of claim 11, wherein theprocessor is configured to generate the depiction of the firstanatomical structure by importing portions of CT-scans of the patient'sbody.
 20. The system of claim 11, wherein the first anatomical structurecomprises a human heart valve and the processor further is configured togenerate a depiction of a valve leaflet of the human heart valve using aplurality of leaflet attachment points.
 21. A non-transitory,computer-readable storage medium storing instructions thereon that whenexecuted by one or more processors carries out a method of: generating adepiction of a first anatomical structure of a patient's body in which aprosthetic device is to be placed, the depiction including a blood poolvolume communicating with an adjacent anatomical structure; and for adesignated placement of the prosthetic device within the firstanatomical structure, predicting an amount of blood flow obstruction tothe adjacent anatomical structure.
 22. The computer-readable storagemedium of claim 21, wherein the method carried out by the one or moreprocessors further comprises revising the designated placement of theprosthetic device responsive to the predicted amount of blood flowobstruction to the adjacent anatomical structure.
 23. Thecomputer-readable storage medium of claim 21, wherein the method carriedout by the one or more processors further comprises, for a givendesignated placement: determining a cross-sectional area of the bloodpool volume communicating with the adjacent anatomical structure; andpredicting an amount of the blood flow obstruction based on thedetermined cross-sectional area.
 24. The computer-readable storagemedium of claim 21, wherein the method carried out by the one or moreprocessors further comprises: designating placement of the prostheticdevice within the first anatomical structure by selecting a model of theprosthetic device from amongst a plurality of models; and predicting,for the selected model, the amount of blood flow obstruction to theadjacent anatomical structure.
 25. The computer-readable storage mediumof claim 21, wherein the method carried out by the one or moreprocessors further comprises, for a depiction of the first anatomicalstructure showing an aortic root of the patient and the adjacentanatomical structure showing the coronary artery ostia, predicting theamount of obstruction of the blood flow to the coronary artery ostiabased on the designated placement of the prosthetic device within theaortic root.
 26. The computer-readable storage medium of claim 21,wherein the method carried out by the one or more processors furthercomprises, for a depiction of the first anatomical structure showing amitral valve annulus of the patient and the adjacent anatomicalstructure showing a left ventricular outflow tract, predicting theamount of obstruction of the blood flow through the left ventricularoutflow tract based on the designated placement of the prosthetic devicewithin the mitral valve annulus.
 27. The computer-readable storagemedium of claim 21, wherein the method carried out by the one or moreprocessors further comprises: generating a revised depiction of thefirst anatomical structure showing a deformation of the first anatomicalstructure caused by the designated placement of the prosthetic device,the revised depiction including an impact of the deformation on theblood pool volume and the adjacent anatomical structure.
 28. Thecomputer-readable storage medium of claim 27, wherein the method carriedout by the one or more processors further comprises computing thedeformation of the first anatomical structure using finite elementanalysis.
 29. The computer-readable storage medium of claim 21, whereinthe method carried out by the one or more processors further comprisesimporting portions of CT-scans of the patient's body.
 30. Thecomputer-readable storage medium of claim 21, wherein the method carriedout by the one or more processors further comprises, for the firstanatomical structure comprising a human heart valve, generating adepiction of a valve leaflet of the human heart valve using a pluralityof leaflet attachment points.